Diffusion models have shown their effectiveness in generation tasks by well-approximating the underlying probability distribution. However, diffusion models are known to suffer from an amplified inherent bias from the training data in terms of fairness. While the sampling process of diffusion models can be controlled by conditional guidance, previous works have attempted to find empirical guidance to achieve quantitative fairness. To address this limitation, we propose a fairness-aware sampling method called \textit{attribute switching} mechanism for diffusion models. Without additional training, the proposed sampling can obfuscate sensitive attributes in generated data without relying on classifiers. We mathematically prove and experimentally demonstrate the effectiveness of the proposed method on two key aspects: (i) the generation of fair data and (ii) the preservation of the utility of the generated data.

翻译：扩散模型通过精确逼近底层概率分布，在生成任务中展现出卓越性能。然而，扩散模型存在放大训练数据固有偏差的公平性问题。虽然扩散模型的采样过程可通过条件引导进行调控，但现有研究多依赖经验性引导来实现量化公平。为突破这一局限，本文提出一种面向扩散模型的公平感知采样方法——\textit{属性切换}机制。该方法无需额外训练，且不依赖分类器即可在生成数据中混淆敏感属性。我们从数学证明与实验验证两个维度论证了该方法的有效性：(i) 生成数据的公平性保障；(ii) 生成数据效用的保持。